Parallel energy-efficient coverage optimization with maximum entropy clustering in wireless sensor networks

Energy constraint is an important issue in wireless sensor networks. This paper proposes a parallel energy-efficient coverage optimization mechanism to optimize the positions of mobile sensor nodes based on maximum entropy clustering in large-scale wireless sensor networks. According to the models of coverage and energy, stationary nodes are partitioned into clusters by maximum entropy clustering. After identifying the boundary node of each cluster, the sensing area is divided for parallel optimization. A numerical algorithm is adopted to calculate the coverage metric of each cluster, while the lowest cost paths of the inner cluster are used to define the energy metric in which Dijkstra’s algorithm is utilized. Then cluster heads are assigned to perform parallel particle swarm optimization to maximize the coverage metric and minimize the energy metric where a weight coefficient between the two metrics is employed to achieve a tradeoff between coverage area and energy efficiency. Simulations of the optimization mechanism and a target tracking application verify that coverage performance can be guaranteed by choosing a proper weight coefficient for each cluster and energy efficiency is enhanced by parallel energy-efficient optimization.     

Enabling energy-efficient and lossy-aware data compression in wireless sensor networks by multi-objective evolutionary optimization

Nodes of wireless sensor networks (WSNs) are typically powered by batteries with a limited capacity. Thus, energy is a primary constraint in the design and deployment of WSNs. Since radio communication is in general the main cause of power consumption, the different techniques proposed in the literature to improve energy efficiency have mainly focused on limiting transmission/reception of data, for instance, by adopting data compression and/or aggregation. The limited resources available in a sensor node demand, however, the development of specifically designed algorithms. To this aim, we propose an approach to perform lossy compression on single node based on a differential pulse code modulation scheme with quantization of the differences between consecutive samples. Since different combinations of the quantization process parameters determine different trade-offs between compression performance and information loss, we exploit a multi-objective evolutionary algorithm to generate a set of combinations of these parameters corresponding to different optimal trade-offs. The user can therefore choose the combination with the most suitable trade-off for the specific application. We tested our lossy compression approach on three datasets collected by real WSNs. We show that our approach can achieve significant compression ratios despite negligible reconstruction errors. Further, we discuss how our approach outperforms LTC, a lossy compression algorithm purposely designed to be embedded in sensor nodes, in terms of compression rate and complexity.     

无线传感器网络节点自调度冗余覆盖算法

对于能量受限的无线传感器网络,延长网络存活的时间很关键。针对这个问题,提出了一种基于能量均衡的传感器节点自调度冗余覆盖协议（SRCP）,通过仿真实验对该算法的有关性能进行了评价,性能评价表明：这种算法能有效使用节点能力,延长网络存活时间。     

无线传感器网络TDMA调度的能量-时延Pareto优化

针对多到一数据传输模式的无线传感器网络,提出了多目标TDMA（时分多址）调度优化模型,考虑了数据包的时延和节点状态切换导致的能量消耗,合理地建立了TDMA调度问题和进化搜索算法问的映射关系,并设计了基于微粒群的Pareto优化算法．仿真实验表明,该算法可以有效地找到一组能量和时延目标的Pareto优化解,其结果优于图着色算法．     

Metaheuristics for maximization of obstacles constrained area coverage in heterogeneous wireless sensor networks

Wireless Sensor Networks (WSNs) collect and transfer environmental data from a predefined field to a base station to be processed and analyzed. A major problem in designing WSNs is coverage maximization, in which a given number of sensor nodes must be deployed in a way that maximizes area coverage of a given network, without violating practical constraints. This is a known NP-hard problem and thus requires metaheuristic approaches for practical problem sizes.Two metaheuristics, namely Genetic Algorithm and Particle Swarm Optimization are proposed to tackle this problem. Our new contributions include a partial use of heuristic initialization, new fitness function, modified virtual force algorithm, addition of a uniform deceleration to the calculation of inertia weight and addition of the influence of sub-populations’ head individuals. The proposed algorithms are comprehensively experimented and compared with the current state-of-the-art for the equivalent problem without obstacles. Experimental results not only suggest which algorithms should be applied to which cases, but also provide insights into parameter settings, effects of heuristic initialization and effects of virtual force algorithm in each case. These conclusions are meaningful for our future research on obstacles constrained area coverage problems related to connectivity and lifetime of WSNs.     

Energy efficient coverage control in wireless sensor networks based on multi-objective genetic algorithm

Due to the constrained energy and computational resources available to sensor nodes, the number of nodes deployed to cover the whole monitored area completely is often higher than if a deterministic procedure were used. Activating only the necessary number of sensor nodes at any particular moment is an efficient way to save the overall energy of the system. A novel coverage control scheme based on multi-objective genetic algorithm is proposed in this paper. The minimum number of sensors is selected in a densely deployed environment while preserving full coverage. As opposed to the binary detection sensor model in the previous work, a more precise detection model is applied in combination with the coverage control scheme. Simulation results show that our algorithm can achieve balanced performance on different types of detection sensor models while maintaining high coverage rate. With the same number of deployed sensors, our scheme compares favorably with the existing schemes.     

无线传感网络覆盖的多步长粒子群优化研究

为了改善无线传感网络的网络性能,提高网络的覆盖率,实现网络覆盖范围的最大化,延长网络寿命,在多步长粒子群算法的基础上提出以网络覆盖率为优化目标的覆盖优化策略。该策略针对不同的个体情况改变粒子的最大飞行速度,实现粒子的多步长搜索,有效地解决了粒子群算法容易出现的早熟问题。仿真实验表明,与粒子群算法相比,多步长粒子群算法的有效覆盖率由74.76%提高到82.66%,到达收敛的迭代次数由360次减少到283次,收敛速度提高了21.4%。因此多步长粒子群优化策略比粒子群算法在无线传感网络覆盖优化上具有更好的效果。     

Multi-objective optimization for coverage control in wireless sensor network with adjustable sensing radius

In this paper, the problem of maintaining sensing coverage by keeping a small number of active sensor nodes and a small amount of energy consumption in a wireless sensor network is studied. As opposed to the uniform sensing model previously, we consider a large number of sensors with adjustable sensing radius that are randomly deployed to monitor a target area. A novel coverage control scheme based on elitist non-dominated sorting genetic algorithm (NSGA-II) is proposed in a heterogeneous sensor network. By devising a cluster-based architecture, the algorithm is applied in a distributed way. Furthermore, an ameliorated binary coding is addressed to represent both sensing radius adjustment and sensor selection. Numerical and simulation results validate that the procedure to find the optimal balance point among the maximum coverage rate, the least energy consumption, as well as the minimum number of active nodes is fast and effective.     

基于激素调节的传感器网络覆盖算法*

人体内环境的平衡是由各种激素的相互作用来协同调节和控制的,根据抽象出的激素作用机理,提出了一种基于激素调节的传感器网络覆盖算法(HCA),该算法是完全分布式的,节能的传感器网络覆盖算法,算法中节点状态(sleep或active)的选择通过激素来调节和控制,即通过给邻居节点发送激活荷尔蒙或抑制荷尔蒙来刺激或抑制邻居节点成为active状态。仿真实验表明,与DELIC和UC算法相比,该算法既能有效地保证区域覆盖,又可以使得active状态的节点尽可能少。     

无线传感器网络中多目标优化节点部署模型

针对多跳无线传感器网络中数据采集只采用单目标优化策略带来的问题,提出了一种基于多目标优化的可移动sink节点部署模型.该模型以网络能耗最小和数据延迟最小为优化目标,采用多目标线性规划方法获得节点部署的较优解,在能量消耗和数据收集延迟中取得平衡.仿真结果表明,该模型能够给决策制定者提供更优的无线网络数据采集方案,提高了数据采集的质量.     

MOFCA: Multi-objective fuzzy clustering algorithm for wireless sensor networks

This study introduces a new clustering approach which is not only energy-efficient but also distribution-independent for wireless sensor networks (WSNs). Clustering is used as a means of efficient data gathering technique in terms of energy consumption. In clustered networks, each node transmits acquired data to a cluster-head which the nodes belong to. After a cluster-head collects all the data from all member nodes, it transmits the data to the base station (sink) either in a compressed or uncompressed manner. This data transmission occurs via other cluster-heads in a multi-hop network environment. As a result of this situation, cluster-heads close to the sink tend to die earlier because of the heavy inter-cluster relay. This problem is named as the hotspots problem. To solve this problem, some unequal clustering approaches have already been introduced in the literature. Unequal clustering techniques generate clusters in smaller sizes when approaching the sink in order to decrease intra-cluster relay. In addition to the hotspots problem, the energy hole problem may also occur because of the changes in the node deployment locations. Although a number of previous studies have focused on energy-efficiency in clustering, to the best of our knowledge, none considers both problems in uniformly and non-uniformly distributed networks. Therefore, we propose a multi-objective solution for these problems. In this study, we introduce a multi-objective fuzzy clustering algorithm (MOFCA) that addresses both hotspots and energy hole problems in stationary and evolving networks. Performance analysis and evaluations are done with popular clustering algorithms and obtained experimental results show that MOFCA outperforms the existing algorithms in the same set up in terms of efficiency metrics, which are First Node Dies (FND), Half of the Nodes Alive (HNA), and Total Remaining Energy (TRE) used for estimating the lifetime of the WSNs and efficiency of protocols.     

一种新型无线传感器网络节能覆盖方案

针对一种实际地理环境下的生态监测问题,把拓扑控制中的功率控制思想引入到节能覆盖的研究中,建立感知半径之和最小的数学模型,并用遗传算法求解该模型,得到最优覆盖解。最后,对该方案进行能耗分析和仿真实验,结果表明该算法不仅节能,而且可以获得较高覆盖率,降低信道通讯干扰并提高网络的抗毁性。     

无线传感网络中覆盖能效动态控制优化策略

能量约束是无线传感网络测量控制的关键问题之一.本文针对移动节点位置优化问题,提出了无线传感网络通信能耗评价指标,采用微粒群优化策略更新节点位置,使无线传感网络具有更强的灵活性和能效性.利用Dijkstra算法获得网络最优通信路径计算能耗评价指标.采用动态能量控制策略使空闲节点进入睡眠状态减少网络运行能耗.通过优化能量指标降低了通信能耗,实现了无线传感网络覆盖与通信能量消耗的合理均衡.对移动目标跟踪仿真表明,覆盖能效优化算法与动态能量控制策略相结合提高了无线传感网络覆盖的能效性.     

大规模无线传感器网络定位算法研究

在同一仿真平台上比较了3种分布式定位算法,Ad Hoc positioning,Robust positioning和N-Hopmultilateration,并介绍了每种算法的基本原理和实现方法,抽象出一种适用于大规模无线传感器网络的通用三阶段分布式定位结构体系。仿真结果显示了3种算法在不同场景下的定位误差情况,比较了3种算法的优劣,同时,也对不同的网络环境参数对网络定位性能的影响做出了分析。     

基于时间同步的无线传感器网络覆盖控制优化算法*

为了实现网络覆盖控制的优化,减少能耗,提出了一种分布的、高效节能、与节点位置无关的传感器网络覆盖算法。在该算法中,节点与邻居交换信息,并通过能量大小竞选工作节点,节点根据自身与工作节点的距离判断决定进入工作状态或休眠状态,并采用在生成树中广播时间同步算法使工作节点网络达到时间同步。仿真结果比较表明,该算法能够明显减少工作节点数,从而减少能量消耗,延长网络寿命。     

On the efficient and fast response for sensor deployment in sparse wireless sensor networks

Wireless sensor networks have recently become new techniques and popular research issues. A wireless sensor network consists of a large number of sensor nodes that have the capabilities of sensing, computing and wireless transmission. Wireless sensor networks (namely WSNs) assist people in working under dangerous environments, provide long-term target observations and track on moving objects. Consequently, WSNs decrease risk and increase efficiency. Although WSNs have been studied extensively, several problems should be addressed, such as sensor-deployment policy, data aggregation/fusion issue, and data transmission issue. An efficient sensor-deployment approach could decrease cost, minimize transmission delay and reduce time complexity. Most studies have proposed the probability-based sensor-deployment policies to monitor an overall area. However, not the entire network is interested to be sensed/monitored. Monitoring of an entire area brings several disadvantages: (1) high cost of placing large number of sensors, (2) long delay of data transmission, (3) slow response and (4) unnecessary data aggregation. Furthermore, previous works were lack of considering the difference between the sensing and the transmission radii, and then yield inaccurate analysis. This work thus proposes an efficient sensor placement approach (namely ESP) for a sparse interested area with considering of obstructers that block the data transmission and sensing signal. Additionally, the issue of different radii of sensing and transmission is analyzed in detail. Numerical results demonstrate that the proposed ESP approach requires the least number of sensor nodes under various network sizes and different number of obstacles. Simulation results indicate that the number of sensor nodes decreases when the sensing or transmission radius increases. The running time of ESP, O(K²), is also analyzed, which is better than that of the probability-based approaches, O(N²), where K is the number of interested grids and N is the number of grids.     

无线传感器网络覆盖问题的研究进展*

分析了无线传感器网络的网络特征以及影响网络覆盖的重要因素,总结和评估了近年来提出的覆盖机制,同时对该领域尚存问题以及发展趋势进行了讨论。     

异构无线传感器网络中多目标优化节点部署策略

针对异构无线传感器网络节点高密度部署和事件发生存在"热点区域"问题,以区域覆盖率最大和网络能耗最小为优化目标,提出了一种基于多目标优化的二进制粒子群算法,对节点部署进行多目标优化。该算法采用概率感知模型,引入强支配系数使得解分布均匀,结合Pareto最优解选择排序和基于自适应权重的适应度分配,进而获得异构节点部署解。仿真结果表明:该算法能对目标空间进行广泛搜索,与NSGA—Ⅱ算法相比,算法具有良好的收敛性,能有效地提高网络的覆盖率和降低网络能耗。     

SCCP：无线传感器网络自调节圆周覆盖协议*

为了提高节点能量的利用率,延长网络寿命,提出了一种节能的自调节圆周覆盖协议(SCCP)。协议基本思想是,基于圆周覆盖算法,在保证网络覆盖的前提下,通过休眠冗余节点提高网络生存时间。模拟实验结果表明,SCCP在覆盖率性能方面与圆周覆盖算法类似,但能有效节约节点能量,延长网络生存时间。